We use the following simplification approaches as baselines:
(i) BERT-Initialized Transfomer [1]}, where the encoder is initialized with BERT\(_{base}\)  checkpoint and the decoder is randomly initialized. It is the current state-of-the-art for text simplification [2]}.
(ii) EditNTS [3]},https://github.com/yuedongP/EditNTS another state-of-the-art model that uses a neural programmer-interpreter [4]} to predict the edit operation on each word, and then generates the simplified sentence.
(iii) LSTM baseline, a vanilla encoder-decoder model used in [5]} zhang-lapata-2017-sentence.
(iv) Hybrid-NG [6]},https://github.com/shashiongithub/Sentence-Simplification-ACL14 one of the best existing hybrid systems that performs splitting and deletion using a probabilistic model and lexical substitution with a phrase-based machine translation system. We retrained all the models on the Newsla-Auto dataset.
